to learn from. In this case, the team is gathering data from two real-world airports, both of them in Pennsylvania. One has an air traffic control tower, and the other does not. Patrikar says that at those airports the data they hoover up includes visual information from cameras located on a hanger or near the taxiway, spoken communication from the radios, weather data, and more. “We record the entirety of it,” he says.
“It knows the causality of things,” he adds. That means that the AI could learn, for example, that “it was because of the weather that they [a pilot] decided to do this particular thing.” The training the AI received in these scenarios has helped it learn how to navigate a landing in simulation, Patrikar says.
Plus, an AI bringing an aircraft in for a landing at a small, uncontrolled airport must both follow FAA rules as well as other norms when interacting with other planes, Patrikar points out. “One of the ways humans trust each other is with our shared understanding of rules—our social norms,” he says. People on a busy sidewalk might decide how to pass each other by moving to the right, for example, and rules like that apply in aviation that the AI pilot must follow.
To be sure, the Carnegie Mellon researchers are not the only people exploring the new frontier of artificial intelligence that can fly, or help fly, aircraft. The Zipline drone company has been working on a way to
Ummm… Meanwhile…